Genomic prediction based on data from three layer lines: a comparison between linear methods.

Mario Pl Calus,Jack J Windig,Addie Vereijken,Jan Ten Napel,Jeroen Visscher,Heyun Huang

doi:10.1186/s12711-014-0057-5

Abstract

BackgroundThe prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction.MethodsCompared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we evaluated the following genomic prediction models: genome-enabled BLUP (GBLUP), ridge regression BLUP (RRBLUP), principal component analysis followed by ridge regression (RRPCA), BayesC and Bayesian stochastic search variable selection. Prediction accuracy was measured as the correlation between predicted breeding values and observed phenotypes divided by the square root of the heritability. The data used concerned laying hens with phenotypes for number of eggs in the first production period and known genotypes. The hens were from two closely-related brown layer lines (B1 and B2), and a third distantly-related white layer line (W1). Lines had 1004 to 1023 training animals and 238 to 240 validation animals. Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms.ResultsGenomic prediction models yielded 0.13 to 0.16 higher accuracies than pedigree-based BLUP. When excluding the line itself from the training dataset, genomic predictions were generally inaccurate. Use of multiple lines marginally improved prediction accuracy for B2 but did not affect or slightly decreased prediction accuracy for B1 and W1. Differences between models were generally small except for RRPCA which gave considerably higher accuracies for B2. Correlations between genomic predictions from different methods were higher than 0.96 for W1 and higher than 0.88 for B1 and B2. The greater differences between methods for B1 and B2 were probably due to the lower accuracy of predictions for B1 (~0.45) and B2 (~0.40) compared to W1 (~0.76).ConclusionsMulti-line genomic prediction did not affect or slightly improved prediction accuracy for closely-related lines. For distantly-related lines, multi-line genomic prediction yielded similar or slightly lower accuracies than single-line genomic prediction. Bayesian variable selection and GBLUP generally gave similar accuracies. Overall, RRPCA yielded the greatest accuracies for two lines, suggesting that using PCA helps to alleviate the “n ≪ p” problem in genomic prediction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0057-5) contains supplementary material, which is available to authorized users.

Highlights

The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction
The first strategy may be more achieved with a model that allows for differential shrinkage, while the second strategy may be more achieved with a genome-enabled BLUP (GBLUP) or regression BLUP (RRBLUP) type of model
BLUP: conventional BLUP using a pedigree based relationship matrix; G-BLUP: Genome-enabled Best Linear Unbiased Prediction (G-BLUP); RRBLUP: Ridge Regression BLUP; RRPCA: Ridge Regression with Principal component analysis (PCA) reduction; BayesSSVS: Bayesian Stochastic Search Variable Selection; BayesC; 1approximated SE of the accuracies of the genomic prediction models ranged from 0.096-0.102; 2for BLUP, only the analysis including the line itself was performed, because there are no pedigree relations between lines

Summary

Introduction

The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. Much research effort has been geared towards developing models for genomic prediction (for a review, see [2]) Most of these are linear models, which can allow for different contributions to genetic variance across SNPs by differential shrinkage e.g. BayesA and BayesB [1], BayesC [7], and Bayesian stochastic search variable selection [8,9]. Many studies have compared the performance of different linear genomic prediction models (for a review, see [2]) Most of these comparisons used data of a single breed or line. Despite these differences in strategies, in general the different models yield very similar predictive abilities, which suggests that for within-breed or within-line selection, the strategy that the model uses has generally limited impact on the results

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